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Record W2051483215 · doi:10.1063/1.2972126

Time-dependent configuration-interaction calculations of laser-driven dynamics in presence of dissipation

2008· article· en· W2051483215 on OpenAlexfundno aff
Jean Christophe Tremblay, Tillmann Klamroth, Peter Saalfrank

Bibliographic record

VenueThe Journal of Chemical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsnot available
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesDeutsche Forschungsgemeinschaft
KeywordsDephasingDissipationNanosecondPhysicsLaserFemtosecondFormalism (music)GaussianTime evolutionStatistical physicsRelaxation (psychology)Quantum mechanics

Abstract

fetched live from OpenAlex

Correlated, multielectron dynamics of "open" electronic systems within the fixed-nuclei approximation are treated here within explicitly time-dependent configuration-interaction schemes. Specifically, we present simulations of laser-pulse driven excitations of selected electronic states of LiCN in the presence of energy and phase relaxation. The evolution of the system is studied using open-system density matrix theory, which embeds naturally in the time-dependent configuration-interaction singles (doubles) formalism. Different models for dissipation based on the Lindblad semigroup formalism are presented. These models give rise to lifetimes for energy relaxation ranging from a few hundreds of femtoseconds to several nanoseconds. Pure dephasing is treated using a Kossakowski-like Gaussian model, proceeding on similar time scales. The pulse lengths employed range from very short (tens of femtoseconds) to very long (several nanoseconds). To make long-time propagations tractable, the quasiresonant approximation is used. The results show that despite the loss of efficiency, selective dipole switching can still be achieved in the presence of dissipation when using appropriately designed laser pulses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.272
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations63
Published2008
Admission routes1
Has abstractyes

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